Strengths and limitations of assessing influenza vaccine effectiveness using routinely collected, passive surveillance data in Ontario, Canada, 2007 to 2012: balancing efficiency versus quality

Heterogeneity of Circulating Influenza Viruses and Their Impact on Influenza Virus Vaccine Effectiveness During the Influenza Seasons 2016/17 to 2018/19 in Austria

The constantly changing pattern in the dominance of viral strains and their evolving subclades during the seasons substantially influences influenza vaccine effectiveness (IVE). In order to further substantiate the importance of detailed data of genetic virus characterization for IVE estimates during the seasons, we performed influenza virus type and subtype specific IVE estimates. IVE estimates were assessed using a test-negative case-control design, in the context of the intraseasonal changes of the heterogeneous mix of circulating influenza virus strains for three influenza seasons (2016/17 to 2018/19) in Austria. Adjusted overall IVE over the three seasons 2016/17, 2017/18, and 2018/19 were -26, 39, and 63%, respectively. In accordance with the changing pattern of the circulating strains a broad range of overall and subtype specific IVEs was obtained: A(H3N2) specific IVE ranged between -26% for season 2016/17 to 58% in season 2018/19, A(H1N1)pdm09 specific IVE was 25% for the season 2017/18 and 65% for the season 2018/19 and Influenza B specific IVE for season 2017/18 was 45%. The results obtained in our study over the three seasons demonstrate the increasingly complex dynamic of the ever changing genetic pattern of the circulating influenza viruses and their influence on IVE estimates. This emphasizes the importance of detailed genetic virus surveillance for reliable IVE estimates.

The Use of Test-negative Controls to Monitor Vaccine Effectiveness: A Systematic Review of Methodology

BACKGROUND

The test-negative design is an increasingly popular approach for estimating vaccine effectiveness (VE) due to its efficiency. This review aims to examine published test-negative design studies of VE and to explore similarities and differences in methodological choices for different diseases and vaccines.

METHODS

We conducted a systematic search on PubMed, Web of Science, and Medline, for studies reporting the effectiveness of any vaccines using a test-negative design. We screened titles and abstracts and reviewed full texts to identify relevant articles. We created a standardized form for each included article to extract information on the pathogen of interest, vaccine(s) being evaluated, study setting, clinical case definition, choices of cases and controls, and statistical approaches used to estimate VE.

RESULTS

We identified a total of 348 articles, including studies on VE against influenza virus (n = 253), rotavirus (n = 48), pneumococcus (n = 24), and nine other pathogens. Clinical case definitions used to enroll patients were similar by pathogens of interest but the sets of symptoms that defined them varied substantially. Controls could be those testing negative for the pathogen of interest, those testing positive for nonvaccine type of the pathogen of interest, or a subset of those testing positive for alternative pathogens. Most studies controlled for age, calendar time, and comorbidities.

CONCLUSIONS

Our review highlights similarities and differences in the application of the test-negative design that deserve further examination. If vaccination reduces disease severity in breakthrough infections, particular care must be taken in interpreting vaccine effectiveness estimates from test-negative design studies.

Estimation of seasonal influenza vaccine effectiveness using data collected in primary care in France: comparison of the test-negative design and the screening method

OBJECTIVES

We discussed which method between the test-negative design (TND) and the screening method (SM) could provide more robust real-time and end-of-season vaccine effectiveness (VE) estimates using data collected from routine influenza surveillance in primary care.

METHODS

We used data collected during two influenza seasons, 2014-15 and 2015-16. Using the SM, we estimated end-of-season VE in preventing medically attended influenza-like illness and laboratory-confirmed influenza among the population at risk. Using the TND, we estimated end-of-season VE in preventing influenza among both the general and the at-risk population. We estimated real-time VE using both methods.

RESULTS

For the SM, the overall adjusted end-of-season VE was 24% (95% confidence interval (CI), 16 to 32) and 12% (95% CI, -16 to 33) during season 2014-15, and 53% (95% CI, 44 to 60) and 47% (95% CI, 23 to 64) during season 2015-16, in preventing influenza-like illness and laboratory-confirmed influenza, respectively. For the TND, the overall adjusted end-of-season VE was -17% (95% CI, -79 to 24) and -38% (95% CI, -199 to 13) in 2014-15, and 10% (95% CI, -31 to 39) and 18% (95% CI, -33 to 50) in 2015-16, among the general and at-risk population, respectively. Real-time VE estimates obtained through the TND showed more variability across each season and lower precision than those estimated with the SM.

CONCLUSIONS

Although the worldwide use of the TND allows for comparison of overall VE estimates among countries, the SM performs better in providing robust real-time VE estimates among the population at risk.

Influenza vaccine effectiveness estimates in the Dutch population from 2003 to 2014: The test-negative design case-control study with different control groups

Information about influenza vaccine effectiveness (IVE) is important for vaccine strain selection and immunization policy decisions. The test-negative design (TND) case-control study is commonly used to obtain IVE estimates. However, the definition of the control patients may influence IVE estimates. We have conducted a TND study using the Dutch Sentinel Practices of NIVEL Primary Care Database which includes data from patients who consulted the General Practitioner (GP) for an episode of acute influenza-like illness (ILI) or acute respiratory infection (ARI) with known influenza vaccination status. Cases were patients tested positive for influenza virus. Controls were grouped into those who tested (1) negative for influenza virus (all influenza negative), (2) negative for influenza virus, but positive for respiratory syncytial virus, rhinovirus or enterovirus (non-influenza virus positive), and (3) negative for these four viruses (pan-negative). We estimated the IVE over all epidemic seasons from 2003/2004 through 2013/2014, pooled IVE for influenza vaccine partial/full matched and mismatched seasons and the individual seasons using generalized linear mixed-effect and multiple logistic regression models. The overall IVE adjusted for age, GP ILI/ARI diagnosis, chronic disease and respiratory allergy was 35% (95% CI: 15-48), 64% (95% CI: 49-75) and 21% (95% CI: -1 to 39) for all influenza negative, non-influenza virus positive and pan-negative controls, respectively. In both the main and subgroup analyses IVE estimates were the highest using non-influenza virus positive controls, likely due to limiting inclusion of controls without laboratory-confirmation of a virus causing the respiratory disease.

Detailed Report on 2014/15 Influenza Virus Characteristics, and Estimates on Influenza Virus Vaccine Effectiveness from Austria's Sentinel Physician Surveillance Network

BACKGROUND

Influenza vaccine effectiveness (VE) is influenced by the antigenic similarity between vaccine- and circulating strains.

MATERIAL AND METHODS

This paper presents data obtained by the Austrian sentinel surveillance system on the evolution of influenza viruses during the season 2014/15 and its impact on influenza vaccine effectiveness in primary care in Austria as estimated by a test-negative case control design. VE estimates were performed for each influenza virus type/subtype, stratified by underlying diseases and adjusted for age, sex and calendar week of infection.

RESULTS

Detailed genetic and antigenic analyses showed that circulating A(H3N2) viruses were genetically distinct from the 2014/15 A(H3N2) vaccine component indicating a profound vaccine mismatch. The Influenza A(H1N1)pdm09 viruses were antigenically conserved and matched the respective vaccine component. Influenza B viruses were lineage-matched B/Yamagata viruses with a clade-level variation. Consistent with substantial vaccine mismatch for the A(H3N2) viruses a crude overall VE of only 47% was estimated, whereas the VE estimates for A(H1N1)pdm09 were 84% and for influenza B viruses 70%. Increased VE estimates were obtained after stratification by underlying diseases and adjustment for the covariates sex and age, whereby the adjustment for the calendar week of infection was the covariate exerting the highest influence on adjusted VE estimates.

CONCLUSION

In summary, VE data obtained in this study underscore the importance to perform VE estimates in the context of detailed characterization of the contributing viruses and also demonstrate that the calendar week of influenza virus infection is the most important confounder of VE estimates.